Interfailure Data with Constant Hazard Function in the Presence of Change-Points

Abstract

Markov Chain Monte Carlo (MCMC) methods are used to perform a Bayesian analysis for interfailure data with constant hazard function in the presence of one or more change-points. We also present some Bayesian criteria to discriminate different models. The methodology is illustrated with a data set originally reported in Maguire, Pearson and Wynn [8].

Keywords

• constant hazard
• change-points
• Gibbs sampling
• MCMC algorithms